Optimized Fuzzy Thermal Management of an Open Cathode Fuel Cell System
Bibliographic record
Abstract
Temperature control has an important part to play in the performance and durability of an open cathode proton exchange membrane fuel cell (PEMFC). This paper puts forward an optimized fuzzy temperature controller with the aim of controlling the cooling fan to provide stable operating conditions in a 500-W Horizon PEMFC. In this respect, an electrochemical model and a thermal model are calibrated for the mentioned PEMFC by means of an optimization algorithm in the first place. Subsequently, a fuzzy logic controller (FLC) is designed and optimized to regulate the temperature by considering the operating current and temperature error as inputs and the fan duty cycle as the only output. The temperature error is determined by the difference between the actual temperature and the reference one. The main idea is to control the fan, which is responsible for water removing, stoichiometry, and temperature regulation, to achieve the reference temperature as fast as possible. The final results of this work indicate the effectiveness of the proposed FLC in reaching the set temperature. The proposed controller can be used in the maximum power point tracking of a PEMFC since this point is reached in a particular stable temperature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".